Talk for the WARN-D Friday Science Meeting
February 16, 2024
the secret reason:
“The quiet revolution of numerical weather prediction” (Bauer, Thorpe, and Brunet 2015)
We should be looking for the true model!
Maybe you do not know how to model with ARIMA…
I suspect it is more likely to depend on the skill of the analyst … these authors are more at home with simple procedures than with Box-Jenkins. (Chatfield)
Our Empirical evidence disagrees
Of course we do
might be useful for Dr Chatfield to read some of the psychological literature quoted in the main paper, and he can then learn a little more about biases
| Competition | Year | N° Time Series | Insights/Novelty |
|---|---|---|---|
| M1 | 1982 | 1001 |
|
| M2 | 1993 | 29 |
|
| M3 | 2000 | 3003 |
|
| M4 | 2020 | 100,000 |
|
| M5 | 2021 | 42,000 |
|
| M6 | 2022 | 100 |
|
The beauty of simple models:
Simple models often outperform more complex ones, even on larger data sets (M1, M3)
Simple models are important benchmarks, even in more complex settings
Selecting a single model:
Move away from model selection to model combination
Test stuff
this
Met Office UK & Bauer, Thorpe, and Brunet (2015)
Weather forecasting used probabilistic forecasting surprisingly early:
The probability of rain was much smaller than at other times (Dalton, 1793)
Popularized by Epstein 1969 Stochastic dynamic prediction
https://www.weather.gov/mrx/probeducation
Practically linked to decision theory, e.g., for JITAIs
Highly relevant in healthcare settings in general
Flexible Mixed Model
From
\[ y = X\beta + Z\upsilon + \epsilon \]
to
\[y = ml_{fixed}(X)+Z\upsilon + \epsilon\]